Title: Overcoming Mode Collapse and the Curse of Dimensionality
Abstract: In this talk, I will present our work on overcoming two long-standing problems in machine learning and computer vision:
1. Mode collapse in generative adversarial nets (GANs)
Generative adversarial nets (GANs) are perhaps the most popular class of generative models in use today. Unfortunately, they suffer from the well-documented problem of mode collapse, which the many successive variants of GANs have failed to overcome. I will illustrate why mode collapse happens fundamentally and show a simple way to overcome it, which is the basis of a new method known as Implicit Maximum Likelihood Estimation (IMLE). Whereas conditional GANs can only generate identical images from the same input, conditional IMLE can generate arbitrarily many diverse images from the same input, as shown below.
2. Curse of dimensionality in exact nearest neighbour search
Efficient algorithms for exact nearest neighbour search developed over the past 40 years do not work in high (intrinsic) dimensions, due to the curse of dimensionality. It turns out that this problem is not insurmountable - I will explain how the curse of dimensionality arises and show a simple way to overcome it, which gives rise to a new family of algorithms known as Dynamic Continuous Indexing (DCI).
Bio: Ke Li is a recent Ph.D. graduate from UC Berkeley, where he was advised by Prof. Jitendra Malik, and is currently a Research Scientist at Google and a Member of the Institute for Advanced Study (IAS). He is interested in a broad range of topics in machine learning and computer vision and has worked on nearest neighbour search, generative modelling and Learning to Optimize. He is particularly passionate about tackling long-standing fundamental problems that cannot be tackled with a straightforward application of conventional techniques. He received his Hon. B.Sc. in Computer Science from the University of Toronto in 2014.
